def run_test(w_list, cell_para, spike_source_data):
pop_list = []
p.setup(timestep=1.0, min_delay=1.0, max_delay=3.0)
#input poisson layer
input_size = w_list[0].shape[0]
pop_in = p.Population(input_size, p.SpikeSourceArray, {'spike_times': []})
for j in range(input_size):
pop_in[j].spike_times = spike_source_data[j]
pop_list.append(pop_in)
for w in w_list:
pos_w = np.copy(w)
pos_w[pos_w < 0] = 0
neg_w = np.copy(w)
neg_w[neg_w > 0] = 0
output_size = w.shape[1]
pop_out = p.Population(output_size, p.IF_curr_exp, cell_para)
p.Projection(pop_in,
pop_out,
p.AllToAllConnector(weights=pos_w),
target='excitatory')
p.Projection(pop_in,
pop_out,
p.AllToAllConnector(weights=neg_w),
target='inhibitory')
pop_list.append(pop_out)
pop_in = pop_out
pop_out.record()
run_time = np.ceil(np.max(spike_source_data)[0] / 1000.) * 1000
p.run(run_time)
spikes = pop_out.getSpikes(compatible_output=True)
return spikes
def connect_layers(input_layer,
output_layer,
weights,
i_s,
j_s,
i_e,
j_e,
k_out,
stdp=False,
initial_weight=0,
label_dicts=None):
"""
Connects a neuron of an output layer to the corresponding square of an input
layer. This is a helper function of connect_layer_to_layer()
Returns:
The created projection between the input and output layers
"""
m = input_layer.shape[1]
view_elements = []
i = i_s
while i < i_e:
j = j_s
while j < j_e:
view_elements.append(m * i + j)
j += 1
i += 1
if stdp:
w_max = initial_weight * 15
stdp_shared = sim.native_synapse_type('stdp_synapse')\
(Wmax=w_max * 1000, mu_plus=0.0, mu_minus=1.0)
proj = sim.Projection(input_layer.population[view_elements],
output_layer.population[[k_out]],
sim.AllToAllConnector(), stdp_shared)
ol = int(output_layer.population.label)
il = input_layer.population.label
out_neuron = output_layer.population[k_out]
if label_dicts == None:
for i in range(len(view_elements)):
label = '{}_{}_{}'.format(ol, il, i)
in_neuron = input_layer.population[view_elements[i]]
conn = nest.GetConnections(source=[in_neuron],
target=[out_neuron])
nest.SetStatus(conn, {'label': label, 'weight': weights[i][0]})
else:
for i in range(len(view_elements)):
label = '{}_{}_{}'.format(ol, il, i)
if not label in label_dicts[ol]:
label_dicts[ol][label] = ([], [])
in_neuron = input_layer.population[view_elements[i]]
label_dicts[ol][label][0].append(in_neuron)
label_dicts[ol][label][1].append(out_neuron)
else:
proj = sim.Projection(input_layer.population[view_elements],
output_layer.population[[k_out]],
sim.AllToAllConnector(),
sim.StaticSynapse(weight=weights))
return proj
def create_C2_layers(S2_layers: Dict[float, Sequence[Layer]],
s2_prototype_cells: int) -> List[sim.Population]:
"""
Creates the populations of the C2 layer, one for each S2 prototype cell,
containing only a single cell which max-pools the spikes of all layers of a
prototype.
Parameters:
`S2_layers`: A dictionary containing for each scale a list of S2
layers, one for each prototype cell
`s2_prototype_cells`: The number of S2 prototype cells
Returns:
A list of populations of size one, one population for each prototype
cell
"""
no_inh_w = 17.15 # synapse weight without S2 inhibitions
with_inh_w = 4 * no_inh_w # synapse weight with S2 inhibitions
C2_populations = [sim.Population(1, sim.IF_curr_exp(),
label=str(prot))\
for prot in range(s2_prototype_cells)]
total_connections = sum(
map(lambda ll: ll[0].shape[0] * ll[0].shape[1], S2_layers.values()))
for s2ll in S2_layers.values():
for prot in range(s2_prototype_cells):
sim.Projection(
s2ll[prot].population, C2_populations[prot],
sim.AllToAllConnector(),
sim.StaticSynapse(weight=with_inh_w / total_connections))
return C2_populations
def test_set_array(self):
weight = 0.123
prj = sim.Projection(self.p1, self.p2, sim.AllToAllConnector())
weight_array = np.ones(prj.shape) * weight
prj.set(weight=weight_array)
self.assertTrue((weight_array == prj.get("weight",
format="array")).all())
def test_nest_projection_gaussian():
p1 = pynn.Population(2, pynn.IF_cond_exp())
p2 = pynn.Population(2, pynn.IF_cond_exp())
c = pynn.Projection(p1, p2,
pynn.AllToAllConnector(allow_self_connections=False))
c.set(weight=pynn.random.RandomDistribution('normal', mu=0.5, sigma=0.1))
weights = c.get('weight', format='array')
assert len(weights[weights == 0]) < 1
def connect(self):
n_cells = self.parameters.config.n_cells
n_segments = self.parameters.config.n_segments
# connect populations
pynn.Projection(self.distal_input, self.distal,
pynn.OneToOneConnector(weights=0.025))
for i in range(self.parameters.config.n_columns):
# get "compartments" for all cells in this column
inhibitions = self.inhibitory[i * n_cells:(i + 1) * n_cells]
somas = self.soma[i * n_cells:(i + 1) * n_cells]
proximal_input = pynn.PopulationView(self.proximal_input, [i])
# set up connections with columnar symmetry
pynn.Projection(inhibitions,
somas,
pynn.DistanceDependentProbabilityConnector(
'd>=1', weights=0.2),
target='SYN_2') # 4
pynn.Projection(proximal_input,
somas,
pynn.AllToAllConnector(weights=0.08),
target='SYN_1') # 1
pynn.Projection(proximal_input, inhibitions,
pynn.AllToAllConnector(weights=0.042)) # 2
for j in range(self.parameters.config.n_cells):
# get "compartments" for this specific cell
segments = self.distal[i * n_cells * n_segments + j *
n_segments:i * n_cells * n_segments +
(j + 1) * n_segments]
inhibition = pynn.PopulationView(self.inhibitory,
[i * n_cells + j])
soma = pynn.PopulationView(self.soma, [i * n_cells + j])
# set up connections with cellular symmetry
pynn.Projection(segments,
inhibition,
pynn.AllToAllConnector(weights=0.15),
target='inhibitory') # 3
pynn.Projection(segments,
soma,
pynn.AllToAllConnector(weights=0.15),
target='SYN_3') # 3
def setUp(self):
sim.setup()
self.p1 = sim.Population(7, sim.IF_cond_exp())
self.p2 = sim.Population(4, sim.IF_cond_exp())
self.p3 = sim.Population(5, sim.IF_curr_alpha())
self.syn_rnd = sim.StaticSynapse(weight=0.123, delay=0.5)
self.syn_a2a = sim.StaticSynapse(weight=0.456, delay=0.4)
self.random_connect = sim.FixedNumberPostConnector(n=2)
self.all2all = sim.AllToAllConnector()
def connect(self):
"""Setup connections between populations"""
params = self.parameters.projections
# generate weights with normal distributed jitter and set up stimulus
w = params.stimulus.weight + np.random.normal(
0, params.stimulus.jitter, len(self.columns))
stimulus_connector = pynn.OneToOneConnector(weights=w)
pynn.Projection(self.stimulus, self.columns, stimulus_connector)
# projection to accumulate/count the number of active columns
accumulation_connector = pynn.AllToAllConnector(
weights=params.accumulation.weight)
pynn.Projection(self.columns, self.kill_switch, accumulation_connector)
# projection to inhibit all columns
inhibition_connector = pynn.AllToAllConnector(
weights=params.inhibition.weight)
pynn.Projection(self.kill_switch,
self.columns,
inhibition_connector,
target='inhibitory')
# forward inhibition
forward_inhibition_connector = pynn.FixedProbabilityConnector(
params.forward_inhibition.probability,
weights=params.forward_inhibition.weight)
pynn.Projection(self.stimulus,
self.columns,
forward_inhibition_connector,
target='inhibitory')
# calculate connectivity matrix
n_columns = self.parameters.populations.columns.size
n_inputs = self.parameters.config.input_size
self.connections = (np.random.uniform(0, 1, n_columns * n_inputs) >
0.60).reshape(len(self.columns),
n_inputs).astype(np.int64)
self.permanences = np.random.normal(.3, .05,
n_columns * n_inputs).reshape(
len(self.columns), n_inputs)
def test_ticket240():
nest = pyNN.nest
nest.setup(threads=4)
parameters = {'Tau_m': 17.0}
p1 = nest.Population(4, nest.IF_curr_exp())
p2 = nest.Population(5, nest.native_cell_type("ht_neuron")(**parameters))
conn = nest.AllToAllConnector()
syn = nest.StaticSynapse(weight=1.0)
prj = nest.Projection(p1, p2, conn, syn, receptor_type='AMPA') # This should be a nonstandard receptor type but I don't know of one to use.
connections = prj.get(('weight',), format='list')
assert len(connections) > 0
def test_single_presynaptic_and_single_postsynaptic_neuron(self):
prj = sim.Projection(self.p4, self.p4, sim.AllToAllConnector(),
synapse_type=sim.StaticSynapse(weight=0.123))
assert prj.shape == (1, 1)
weight = 0.456
prj.set(weight=weight)
self.assertEqual(prj.get("weight", format="array")[0][0], weight)
weight_array = numpy.ones(prj.shape) * weight
prj.set(weight=weight_array)
self.assertTrue((weight_array == prj.get("weight", format="array")).all())
def create_brain():
"""
Initializes PyNN with the minimal neuronal network
"""
sim.setup(timestep=0.1,
min_delay=0.1,
max_delay=20.0,
threads=1,
rng_seeds=[1234])
# Following parameters were taken from the husky braitenberg brain experiment (braitenberg.py)
SENSORPARAMS = {
'cm': 0.025,
'v_rest': -60.5,
'tau_m': 10.,
'e_rev_E': 0.0,
'e_rev_I': -75.0,
'v_reset': -60.5,
'v_thresh': -60.0,
'tau_refrac': 10.0,
'tau_syn_E': 2.5,
'tau_syn_I': 2.5
}
SYNAPSE_PARAMS = {
"weight": 0.5e-4,
"delay": 20.0,
'U': 1.0,
'tau_rec': 1.0,
'tau_facil': 1.0
}
cell_class = sim.IF_cond_alpha(**SENSORPARAMS)
# Define the network structure: 2 neurons (1 sensor and 1 actors)
population = sim.Population(size=2, cellclass=cell_class)
synapse_type = sim.TsodyksMarkramSynapse(**SYNAPSE_PARAMS)
connector = sim.AllToAllConnector()
# Connect neurons
sim.Projection(presynaptic_population=population[0:1],
postsynaptic_population=population[1:2],
connector=connector,
synapse_type=synapse_type,
receptor_type='excitatory')
sim.initialize(population, v=population.get('v_rest'))
return population
def presentStimuli(pres_duration, num_pres_per_stim, num_source, num_target, bright_on_weights, bright_off_weights, bright_lat_weights, dark_on_weights, dark_off_weights, dark_lat_weights, is_repeated=False):
"""
For presenting a stimulus to the target network. Callback is used to switch between presentation rates.
Arguments: num_source
num_target
num_pres_per_stim,
pres_duration
"""
num_stim = 2 # two stimuli 'bright' and 'dark'
total_duration = num_stim * num_pres_per_stim * pres_duration
source_on_pop = pynn.Population(num_source, pynn.SpikeSourcePoisson(), label='source_on_pop')
source_off_pop = pynn.Population(num_source, pynn.SpikeSourcePoisson(), label='source_off_pop')
is_bright, random_on_rates, random_off_rates = getPresentationRatesForCallback(num_stim, num_source, num_pres_per_stim, is_repeated=is_repeated)
bright_target_pop = pynn.Population(num_target, pynn.IF_cond_exp, {'i_offset':0.11, 'tau_refrac':3.0, 'v_thresh':-51.0}, label='target_pop')
dark_target_pop = pynn.Population(num_target, pynn.IF_cond_exp, {'i_offset':0.11, 'tau_refrac':3.0, 'v_thresh':-51.0}, label='target_pop')
bright_on_conn = pynn.Projection(source_on_pop, bright_target_pop, connector=pynn.AllToAllConnector(), synapse_type=pynn.StaticSynapse(weight=bright_on_weights), receptor_type='excitatory')
bright_off_conn = pynn.Projection(source_off_pop, bright_target_pop, connector=pynn.AllToAllConnector(), synapse_type=pynn.StaticSynapse(weight=bright_off_weights), receptor_type='excitatory')
bright_lat_conn = pynn.Projection(bright_target_pop, bright_target_pop, connector=pynn.AllToAllConnector(), synapse_type=pynn.StaticSynapse(weight=bright_lat_weights), receptor_type='inhibitory')
dark_on_conn = pynn.Projection(source_on_pop, dark_target_pop, connector=pynn.AllToAllConnector(), synapse_type=pynn.StaticSynapse(weight=dark_on_weights), receptor_type='excitatory')
dark_off_conn = pynn.Projection(source_off_pop, dark_target_pop, connector=pynn.AllToAllConnector(), synapse_type=pynn.StaticSynapse(weight=dark_off_weights), receptor_type='excitatory')
dark_lat_conn = pynn.Projection(dark_target_pop, dark_target_pop, connector=pynn.AllToAllConnector(), synapse_type=pynn.StaticSynapse(weight=dark_lat_weights), receptor_type='inhibitory')
source_on_pop.record('spikes')
source_off_pop.record('spikes')
bright_target_pop.record(['spikes'])
dark_target_pop.record(['spikes'])
pynn.run(total_duration, callbacks=[PoissonWeightVariation(source_on_pop, random_on_rates, pres_duration), PoissonWeightVariation(source_off_pop, random_off_rates, pres_duration)])
pynn.end()
source_on_spikes = source_on_pop.get_data('spikes').segments[0].spiketrains
source_off_spikes = source_off_pop.get_data('spikes').segments[0].spiketrains
bright_spikes = bright_target_pop.get_data('spikes').segments[0].spiketrains
dark_spikes = dark_target_pop.get_data('spikes').segments[0].spiketrains
return is_bright, source_on_spikes, source_off_spikes, bright_spikes, dark_spikes
def getConnectorType(conn_type, ff_prob=None, lat_prob=None):
"""
For getting the feed-forward and lateral connection types.
Arguments: conn_type, str, choices = ['all_to_all', 'fixed_prob', 'one_of_each']
ff_prob, float, probability of connection for feed-forward
lat_prob, float, probability of connection for lateral
Returns: ff_conn, lat_conn
"""
if conn_type == 'all_to_all':
ff_conn = pynn.AllToAllConnector()
lat_conn = pynn.AllToAllConnector(allow_self_connections=False)
elif conn_type == 'fixed_prob':
if (ff_prob == None) or (lat_prob == None):
print(dt.datetime.now().isoformat() + ' ERROR: ' +
'One of the connections probabilities is "None".')
sys.exit(2)
else:
ff_conn = pynn.FixedProbabilityConnector(
ff_prob, rng=pynn.random.NumpyRNG(seed=1798))
lat_conn = pynn.FixedProbabilityConnector(
lat_prob,
allow_self_connections=False,
rng=pynn.random.NumpyRNG(seed=1916))
elif conn_type == 'one_of_each':
if ff_prob == None:
print(dt.datetime.now().isoformat() + ' ERROR: ' +
'Feed forwards connections probabilities is "None".')
sys.exit(2)
else:
ff_conn = pynn.FixedProbabilityConnector(
ff_prob, rng=pynn.random.NumpyRNG(seed=1798))
lat_conn = pynn.AllToAllConnector(allow_self_connections=False)
else:
print(dt.datetime.now().isoformat() + ' ERROR: ' +
'Unrecognised connection type.')
sys.exit(2)
return ff_conn, lat_conn
def connect_layers(firstLayer, secondLayer):
# === Parameters for STDP mechanism ===
# === Timing dependence ===
# Time constant of the positive part of the STDP curve, in milliseconds.
tau_plus = 20.0
# Time constant of the negative part of the STDP curve, in milliseconds.
tau_minus = 20.0
# Amplitude of the positive part of the STDP curve.
A_plus = 0.006
# Amplitude of the negative part of the STDP curve.
A_minus = 0.0055
# === Weight dependence ===
# Minimum synaptic weight
w_min = 0.0
# Maximum synaptic weight
w_max = 0.1
# Default weight
w_default = 0.0
# === Delay ===
delay = 0.1
# Synapses to use later for testing
global synapses
# The amplitude of the weight change is independent of the current weight.
# If the new weight would be less than w_min it is set to w_min. If it would be greater than w_max it is set to w_max.
stdp_synapse = sim.STDPMechanism(
timing_dependence=sim.SpikePairRule(tau_plus=tau_plus,
tau_minus=tau_minus,
A_plus=A_plus,
A_minus=A_minus),
weight_dependence=sim.AdditiveWeightDependence(w_min=w_min,
w_max=w_max),
dendritic_delay_fraction=1.0,
weight=w_default,
delay=delay)
# Save synapses onto a global variable
synapses = sim.Projection(inputLayer,
outputLayer,
sim.AllToAllConnector(),
synapse_type=stdp_synapse)
def test_record_native_model():
if not have_nest:
raise SkipTest
nest = pyNN.nest
from pyNN.random import RandomDistribution
init_logging(logfile=None, debug=True)
nest.setup()
parameters = {'tau_m': 17.0}
n_cells = 10
p1 = nest.Population(n_cells,
nest.native_cell_type("ht_neuron")(**parameters))
p1.initialize(V_m=-70.0, Theta=-50.0)
p1.set(theta_eq=-51.5)
#assert_arrays_equal(p1.get('theta_eq'), -51.5*numpy.ones((10,)))
assert_equal(p1.get('theta_eq'), -51.5)
print(p1.get('tau_m'))
p1.set(tau_m=RandomDistribution('uniform', low=15.0, high=20.0))
print(p1.get('tau_m'))
current_source = nest.StepCurrentSource(
times=[50.0, 110.0, 150.0, 210.0],
amplitudes=[0.01, 0.02, -0.02, 0.01])
p1.inject(current_source)
p2 = nest.Population(
1,
nest.native_cell_type("poisson_generator")(rate=200.0))
print("Setting up recording")
p2.record('spikes')
p1.record('V_m')
connector = nest.AllToAllConnector()
syn = nest.StaticSynapse(weight=0.001)
prj_ampa = nest.Projection(p2, p1, connector, syn, receptor_type='AMPA')
tstop = 250.0
nest.run(tstop)
vm = p1.get_data().segments[0].analogsignals[0]
n_points = int(tstop / nest.get_time_step()) + 1
assert_equal(vm.shape, (n_points, n_cells))
assert vm.max() > 0.0 # should have some spikes
def recognizer_weights_from(feature_np_array):
"""
Builds a network from the firing rates of the given feature_np_array for the
input neurons and learns the weights to recognize the image through STDP.
"""
in_p = create_spike_source_layer_from(feature_np_array).population
out_p = sim.Population(1, sim.IF_curr_exp(i_offset=5))
synapse = sim.STDPMechanism(
weight=-0.2,
timing_dependence=sim.SpikePairRule(tau_plus=20.0,
tau_minus=20.0,
A_plus=0.01,
A_minus=0.005),
weight_dependence=sim.AdditiveWeightDependence(w_min=0, w_max=0.4))
proj = sim.Projection(in_p, out_p, sim.AllToAllConnector(), synapse)
sim.run(500)
return proj.get('weight', 'array')
def test_record_native_model():
nest = pyNN.nest
from pyNN.random import RandomDistribution
from pyNN.utility import init_logging
init_logging(logfile=None, debug=True)
nest.setup()
parameters = {'Tau_m': 17.0}
n_cells = 10
p1 = nest.Population(n_cells, nest.native_cell_type("ht_neuron"),
parameters)
p1.initialize('V_m', -70.0)
p1.initialize('Theta', -50.0)
p1.set('Theta_eq', -51.5)
assert_equal(p1.get('Theta_eq'), [-51.5] * 10)
print p1.get('Tau_m')
p1.rset('Tau_m', RandomDistribution('uniform', [15.0, 20.0]))
print p1.get('Tau_m')
current_source = nest.StepCurrentSource({
'times': [50.0, 110.0, 150.0, 210.0],
'amplitudes': [0.01, 0.02, -0.02, 0.01]
})
p1.inject(current_source)
p2 = nest.Population(1, nest.native_cell_type("poisson_generator"),
{'rate': 200.0})
print "Setting up recording"
p2.record()
p1._record('V_m')
connector = nest.AllToAllConnector(weights=0.001)
prj_ampa = nest.Projection(p2, p1, connector, target='AMPA')
tstop = 250.0
nest.run(tstop)
n_points = int(tstop / nest.get_time_step()) + 1
assert_equal(p1.recorders['V_m'].get().shape, (n_points * n_cells, 3))
id, t, v = p1.recorders['V_m'].get().T
assert v.max() > 0.0 # should have some spikes
def test_native_stdp_model():
nest = pyNN.nest
from pyNN.utility import init_logging
init_logging(logfile=None, debug=True)
nest.setup()
p1 = nest.Population(10, nest.IF_cond_exp())
p2 = nest.Population(10, nest.SpikeSourcePoisson())
stdp_params = {'Wmax': 50.0, 'lambda': 0.015, 'weight': 0.001}
stdp = nest.native_synapse_type("stdp_synapse")(**stdp_params)
connector = nest.AllToAllConnector()
prj = nest.Projection(p2, p1, connector, receptor_type='excitatory',
synapse_type=stdp)
def test_native_stdp_model():
nest = pyNN.nest
from pyNN.utility import init_logging
init_logging(logfile=None, debug=True)
nest.setup()
p1 = nest.Population(10, nest.IF_cond_exp)
p2 = nest.Population(10, nest.SpikeSourcePoisson)
stdp_params = {'Wmax': 50.0, 'lambda': 0.015}
stdp = nest.NativeSynapseDynamics("stdp_synapse", stdp_params)
connector = nest.AllToAllConnector(weights=0.001)
prj = nest.Projection(p2,
p1,
connector,
target='excitatory',
synapse_dynamics=stdp)
training_spiketrains = [[s for s in st] for st in training_pair[1]]
C2_populations, compound_C2_population =\
create_C2_populations(training_spiketrains)
out_p = sim.Population(1, sim.IF_curr_exp(tau_refrac=.1))
stdp_weight = 7 / s2_prototype_cells
stdp = sim.STDPMechanism(
weight=stdp_weight,
timing_dependence=sim.SpikePairRule(tau_plus=20.0,
tau_minus=26.0,
A_plus=stdp_weight / 5,
A_minus=stdp_weight / 4.48),
weight_dependence=sim.AdditiveWeightDependence(w_min=0.0,
w_max=15.8 *
stdp_weight))
learn_proj = sim.Projection(compound_C2_population, out_p,
sim.AllToAllConnector(), stdp)
epoch = training_pair[0]
print('Simulating for epoch', epoch)
# Record the spikes for visualization purposes and to count the number of
# fired spikes
# compound_C2_population.record('spikes')
out_p.record(['spikes', 'v'])
# Let the simulation run to "fill" the layer pipeline with spikes
sim.run(40)
# Datastructure for storing the computed STDP weights for this epoch
classifier_weights = []
sim.StaticSynapse(weight=0.002, delay=0.1))
spike_2_conn_for_4 = sim.Projection(spike_source_4, cann_pop[4:5],
sim.OneToOneConnector(),
sim.StaticSynapse(weight=0.002, delay=0.1))
spike_2_conn_for_9 = sim.Projection(spike_source_9, cann_pop[9:10],
sim.OneToOneConnector(),
sim.StaticSynapse(weight=0.002, delay=0.1))
cann_2_cann = sim.Projection(
cann_pop, cann_pop,
sim.FromListConnector(cann_connector, column_names=["weight"]),
sim.StaticSynapse(weight=0.0001, delay=75))
cann_2_inh = sim.Projection(cann_pop,
inhib_pop,
sim.AllToAllConnector(),
sim.StaticSynapse(weight=0.02, delay=0.1),
receptor_type="excitatory")
inh_2_cann = sim.Projection(inhib_pop,
cann_pop,
sim.AllToAllConnector(),
sim.StaticSynapse(weight=0.2, delay=0.1),
receptor_type="inhibitory")
#spike_source.record('spikes')
cann_pop.record(('v', 'spikes'))
inhib_pop.record(('v', 'spikes'))
sim.run(5000.0)
/ (2 * np.power(sig, 2.))))), 2)
cann_connector.append((i, j, weights[i][j]))#, delay_cann2cann))
print("Weight matrix:\n", weights)
spike_times = [1000., 2000.]
cann_pop = sim.Population(n, sim.IF_cond_alpha(**cell_params),
label = "cann_pop")
inhib_pop = sim.Population(1, sim.IF_cond_alpha(**cell_params),
label = "inhib_pop")
spike_source = sim.Population(1, sim.SpikeSourceArray(
spike_times = spike_times))
spike_2_conn = sim.Projection(spike_source, cann_pop[spiky:spiky+1], sim.AllToAllConnector(),
sim.StaticSynapse(weight = 0.002, delay = 0.1))
cann_2_cann = sim.Projection(cann_pop, cann_pop, sim.FromListConnector(cann_connector, column_names=["weight"]),
sim.StaticSynapse(weight = 0.0001, delay = 75))
cann_2_inh = sim.Projection(cann_pop, inhib_pop, sim.AllToAllConnector(),
sim.StaticSynapse(weight = 0.02, delay = 0.1),
receptor_type = "excitatory")
inh_2_cann = sim.Projection(inhib_pop, cann_pop, sim.AllToAllConnector(),
sim.StaticSynapse(weight = 0.2, delay = 0.1),
receptor_type = "inhibitory")
spike_source.record('spikes')
cann_pop.record(('v','spikes'))
######################
###### Synapses #######
stat_syn_res = p.StaticSynapse(weight=5.0, delay=1)
stat_syn_input = p.StaticSynapse(weight=50.0, delay=1)
stat_syn_rout = p.StaticSynapse(weight=0.0, delay=1)
######################
###### Connections #######
res_conn = p.FixedProbabilityConnector(param.res_pconn, rng=param.rng)
inp_conn = p.AllToAllConnector()
rout_conn = p.AllToAllConnector()
connections = {}
connections['r2r'] = p.Projection(reservoir,
reservoir,
res_conn,
synapse_type=stat_syn_res,
receptor_type='excitatory')
connections['inp2r'] = p.Projection(input_neurons,
reservoir,
inp_conn,
synapse_type=stat_syn_input,
receptor_type='excitatory')
'start': 0.0,
'duration': tsim
}
#poissonI_params = {'rate': rateI, 'start': 0.0, 'duration': tsim}
poissonE = sim.Population((1, ),
cellclass=sim.SpikeSourcePoisson,
cellparams=poissonE_params,
label='poissonE')
poissonI = sim.Population((1, ),
cellclass=sim.SpikeSourcePoisson,
cellparams=poissonI_params,
label='poissonI')
myconn = sim.AllToAllConnector(weights=globalWeight, delays=dt)
### Connections ###
prjE_E = sim.Projection(poissonE, popE, method=myconn, target='excitatory')
prjI_E = sim.Projection(poissonI, popE, method=myconn, target='inhibitory')
prjE_I = sim.Projection(poissonE, popI, method=myconn, target='excitatory')
prjI_I = sim.Projection(poissonI, popI, method=myconn, target='inhibitory')
## Record the spikes ##
popE.record(to_file=False)
popE.record_v(to_file=False)
popI.record(to_file=False)
popE.record_gsyn(to_file=False)
)
'''
weight_distr = p.RandomDistribution(distribution='normal',parameters=[1,0.1])
'''
if run_i == 0:
stdp_weight = 0.0
directory = 'weight%d' % par_start
if not os.path.exists(directory):
os.makedirs(directory)
else:
stdp_weight = np.load('%s/weight_%d.npy' % (directory, (run_i - 1)))
proj_stdp = p.Projection(
#pop_input, pop_output, p.AllToAllConnector(weights = weight_distr),
pop_input,
pop_output,
p.AllToAllConnector(weights=stdp_weight),
synapse_dynamics=p.SynapseDynamics(slow=stdp_model))
for i in range(num_output):
conn_list = list()
for j in range(num_output):
if i != j:
conn_list.append((i, j, -18.1, 1.0))
p.Projection(pop_output,
pop_output,
p.FromListConnector(conn_list),
target='inhibitory')
for i in range(num_train * num_epo):
p.Projection(TeachingPoission[i],
pop_output,
def create_S2_layers(C1_layers: Dict[float, Sequence[Layer]], feature_size,
s2_prototype_cells, refrac_s2=.1, stdp=True,
inhibition=True)\
-> Dict[float, List[Layer]]:
"""
Creates all prototype S2 layers for all sizes.
Parameters:
`layers_dict`: A dictionary containing for each size a list of C1
layers, for each feature one
`feature_size`:
`s2_prototype_cells`:
`refrac_s2`:
`stdp`:
Returns:
A dictionary containing for each size a list of different S2
layers, for each prototype one.
"""
f_s = feature_size
initial_weight = 25 / (f_s * f_s)
weight_rng = rnd.RandomDistribution('normal',
mu=initial_weight,
sigma=initial_weight / 20)
i_offset_rng = rnd.RandomDistribution('normal', mu=.5, sigma=.45)
weights = list(
map(lambda x: weight_rng.next() * 1000, range(4 * f_s * f_s)))
S2_layers = {}
i_offsets = list(
map(lambda x: i_offset_rng.next(), range(s2_prototype_cells)))
ndicts = list(map(lambda x: {}, range(s2_prototype_cells)))
ondicts = list(map(lambda x: {}, range(s2_prototype_cells)))
omdicts = list(map(lambda x: {}, range(s2_prototype_cells)))
for size, layers in C1_layers.items():
n, m = how_many_squares_in_shape(layers[0].shape, (f_s, f_s), f_s)
if stdp:
l_i_offsets = [list(map(lambda x: rnd.RandomDistribution('normal',
mu=i_offsets[i], sigma=.25).next(), range(n * m)))\
for i in range(s2_prototype_cells)]
else:
l_i_offsets = np.zeros((s2_prototype_cells, n * m))
print('S2 Shape', n, m)
layer_list = list(
map(
lambda i: Layer(
sim.Population(n * m,
sim.IF_curr_exp(i_offset=l_i_offsets[i],
tau_refrac=refrac_s2),
structure=space.Grid2D(aspect_ratio=m / n),
label=str(i)), (n, m)),
range(s2_prototype_cells)))
for S2_layer in layer_list:
for C1_layer in layers:
S2_layer.projections[C1_layer.population.label] =\
connect_layer_to_layer(C1_layer, S2_layer, (f_s, f_s), f_s,
[[w] for w in weights[:f_s * f_s]],
stdp=stdp,
initial_weight=initial_weight,
ndicts=ndicts, ondicts=ondicts,
omdicts=omdicts)
S2_layers[size] = layer_list
# Set the labels of the shared connections
if stdp:
t = time.clock()
print('Set shared labels')
for s2_label_dicts in [ndicts, ondicts, omdicts]:
for i in range(s2_prototype_cells):
w_iter = weights.__iter__()
for label, (source, target) in s2_label_dicts[i].items():
conns = nest.GetConnections(source=source, target=target)
nest.SetStatus(conns, {
'label': label,
'weight': w_iter.__next__()
})
print('Setting labels took', time.clock() - t)
if inhibition:
# Create inhibitory connections between the S2 cells
# First between the neurons of the same layer...
inh_weight = -10
inh_delay = .1
print('Create S2 self inhibitory connections')
for layer_list in S2_layers.values():
for layer in layer_list:
sim.Projection(
layer.population, layer.population,
sim.AllToAllConnector(allow_self_connections=False),
sim.StaticSynapse(weight=inh_weight, delay=inh_delay))
# ...and between the layers
print('Create S2 cross-scale inhibitory connections')
for i in range(s2_prototype_cells):
for layer_list1 in S2_layers.values():
for layer_list2 in S2_layers.values():
if layer_list1[i] != layer_list2[i]:
sim.Projection(
layer_list1[i].population,
layer_list2[i].population, sim.AllToAllConnector(),
sim.StaticSynapse(weight=inh_weight,
delay=inh_delay))
if stdp:
# Create the inhibition between different prototype layers
print('Create S2 cross-prototype inhibitory connections')
for layer_list in S2_layers.values():
for layer1 in layer_list:
for layer2 in layer_list:
if layer1 != layer2:
sim.Projection(
layer1.population, layer2.population,
sim.OneToOneConnector(),
sim.StaticSynapse(weight=inh_weight - 1,
delay=inh_delay))
return S2_layers
'cm': 0.5
}
sim.setup()
neuron1 = sim.Population(n, sim.IF_cond_alpha(**cell_params), label="neuron1")
neuron2 = sim.Population(n, sim.IF_cond_alpha(**cell_params), label="neuron2")
generate_spike_times = [
0., 1020., 1040., 1060., 1080., 1100., 1120., 1140., 1160., 2000.
]
spike_source = sim.Population(
n, sim.SpikeSourceArray(spike_times=generate_spike_times))
conn = sim.Projection(spike_source, neuron1, sim.AllToAllConnector(),
sim.StaticSynapse(weight=0.002, delay=1.))
n2n_conn = sim.Projection(neuron1, neuron2, sim.OneToOneConnector(),
sim.StaticSynapse(weight=0.002, delay=1.))
spike_source.record('spikes')
neuron1.record(('v', 'spikes'))
neuron2.record(('v', 'spikes'))
sim.run(5000.0)
#print neuron1.get_spike_counts()
from pyNN.utility.plotting import Figure, Panel
data1 = neuron1.get_data().segments[0]
data2 = neuron1.get_data().segments[0]
#seeds = numpy.arange(numberOfNodes) + int((time.time()*100)%2**32)
# seeds which are same every time, different for each node
seeds = numpy.arange(numberOfNodes)
# bcast, as we can't be sure each node has the same time, and therefore
# different seeds. This way, all nodes get the list from rank=0.
seeds = MPI.COMM_WORLD.bcast(seeds)
#rng = NumpyRNG(seed=seeds[rank], parallel_safe=False, rank=rank,
# num_processes=numberOfNodes)
nest.SetKernelStatus({'rng_seeds': list(seeds)})
## Connections ##
#myConnectorE = sim.AllToAllConnector(weights=globalWeight, delays=0.1)
myConnectorI = sim.AllToAllConnector(weights=globalWeight, delays=0.1)
# Connectors which make the specified number of connections from pre to post
# the inh_gamma_generator sends a independent realization to each post connection
# So this is "as if" there where "num connection" independent inh_gamma_generators
# impinging on the target
myConnectorE_E = sim.FixedNumberPreConnector(int(connectionsE_E) - NumOfConE_E, weights=globalWeight, delays=0.1)
myConnectorE_I = sim.FixedNumberPreConnector(int(connectionsE_I) - NumOfConE_I, weights=globalWeight, delays=0.1)
# a sub-set of the inh_gamma_generaters are silenced after a time "tinit"
myConnectorE_E_silenced = sim.FixedNumberPreConnector(NumOfConE_E, weights=globalWeight, delays=0.1)
myConnectorE_I_silenced = sim.FixedNumberPreConnector(NumOfConE_I, weights=globalWeight, delays=0.1)
#myConnectorI = sim.AllToAllConnector(weights=globalWeight, delays=0.1)
# InhGamma Generators need "_S" (selective) type synapses
# Passing this class to the Projection in a ComposedSynapseType object
sim.setup(timestep=0.1)
# --- Create populations of neurons ------------
populations = {'exc': [], 'inh': []}
for syn_type in ('exc', 'inh'):
populations[syn_type] = [sim.Population(population_size,
sim.IF_cond_exp,
neuron_parameters)
for i in range(n_populations)]
# --- Connect the populations in a chain -------
connector_exc_exc = sim.AllToAllConnector(weights=weight_exc_exc, delays=delay)
connector_exc_inh = sim.AllToAllConnector(weights=weight_exc_inh, delays=delay)
connector_inh_exc = sim.AllToAllConnector(weights=weight_inh_exc, delays=delay)
for i in range(n_populations):
j = (i + 1) % n_populations
prj_exc_exc = sim.Projection(populations['exc'][i], populations['exc'][j],
connector_exc_exc, target='excitatory')
prj_exc_inh = sim.Projection(populations['exc'][i], populations['inh'][j],
connector_exc_inh, target='excitatory')
prj_inh_exc = sim.Projection(populations['inh'][i], populations['exc'][i],
connector_exc_exc, target='inhibitory')
# --- Create and connect stimulus --------------
numpy.random.seed(rng_seed)
# %%CUBA
I_vec, u_vec, s_vec = LIF_CUBA(spikes=spikes_e)
# %% PyNN CUBA
# Setup
sim.setup(timestep=0.1, min_delay=0.1, max_delay=10.0)
IF_sim = sim.Population(1, sim.IF_curr_exp(), label="IF_curr_exp")
IF_sim.record('v')
spike_times = np.arange(50,100,10)
spike_input = sim.Population(1,sim.SpikeSourceArray(spike_times=spike_times),label="Input spikes")
# Connections
w = 1
connections = sim.Projection(spike_input, IF_sim,
connector=sim.AllToAllConnector(),
synapse_type=sim.StaticSynapse(weight=w,delay=0.1),
receptor_type="excitatory")
# Running simulation in MS
IF_sim.record('v')
sim.run(100.0)
# Data
v_data = IF_sim.get_data()
data = IF_sim.get_data().segments[0]
v_cuba = data.filter(name="v")[0]
# Plotting
plt.plot(v_cuba)
